Project Summary Children with severe Early Caregiving Adversities (ECAs) are the most vulnerable to psychopathology as a result of prolonged neglect, abuse, and care disruptions that impact neurodevelopment. It is currently estimated that addressing ECAs would lead to a 29.8% reduction in worldwide psychiatric illness. Existing research, including findings from the original grant of this renewal, has demonstrated that there is a very strong link between ECA exposures and increased risk for psychopathology and altered neurodevelopment at the population level; and yet, given the heterogeneity in ECA populations, there is a critical gap in knowledge regarding how ECAs increase any specific risks to an individual child. The proposed research addresses this significant mental health problem by combining sophisitcated data-analysis methods that use experiential and phenotypic heterogeneity together with longitudinal neuroimaging and behavioral assessments in school-age children. This approach will increase precision when linking ECAs and child outcomes associated with the Research Domain Criteria constructs of Negative Valence and Cognitive Control Systems (NVS/CCS). The overarching goal of the present work is to create an explanatory model for the heterogeneous impact of ECAs on neurodevelopmental trajectories of NVS/CCS. This project's premise is that children exposed to ECAs have highly heterogeneous developmental histories as well as heterogeneous outcomes; therefore, prediction of ECA outcomes requires cutting-edge, sophisticated data analytic methods. We hypothesize that data-driven approaches will 1) more precisely define NVS/CCS outcomes for school-aged children with ECAs, and 2) provide a more robust explanatory model for links between ECAs and NVS/CCS trajectories. Aim 1A subtypes children with a history of ECAs based on 2.5-year developmental trajectories of NVS/CCS. 300 6-8 year old children (250 sampled from previous institutional and foster care; 50 community comparisons) will provide neuroimaging, behavioral, and self/caregiver reports every 15 months for 2.5 years. Biclustering methods will be applied to the baseline and follow-up data to identify homogeneous NVS/CCS final outcome clusters of children. Aim 1B develops an explanatory model to predict developmental trajectory subtypes for children with ECAs, from early life profiles and brain/behavior phenotypes at the time of enrollment. Machine learning methods applied to early life profiles, baseline NVS/CCS profiles, and sex, will predict developmental trajectory subtypes. Aim 2 identifies adverse and protective life events during the 2.5-year assessment period that are predictive of 2.5-year follow-up outcomes for children with ECAs. The inclusion of child-sex and current life- events will identify potential divergence in pathways across middle childhood. This prospective design of children exposed to various ECAs is designed to develop predictive models for ECA trajectory subtypes and outcomes, which can inform our understanding of risk and protective factors in accord with the goals of precision medicine.